# Objectives

Turn intent into measurable action.

Goals Planning Optimization Evaluation

--- ## Intelligence needs objectives **Objectives** is an independent Hugging Face organization focused on how AI systems represent, prioritize, optimize, evaluate, and revise goals. A capable system can generate actions. A useful system should also understand: > **What are we trying to achieve?** --- # The Objective Loop ```text INTENT ↓ OBJECTIVE ↓ CONSTRAINTS ↓ PLAN ↓ ACTION ↓ MEASUREMENT ↓ UPDATE ``` Objectives connect **intent** with **behavior**. --- ## Goal Representation How should an AI system represent what it is trying to accomplish? Possible topics: - explicit goals - subgoals - success criteria - priorities - deadlines - constraints - preferences - stop conditions --- ## Multi-Objective Optimization Real tasks often involve competing goals. For example: ```text maximize quality minimize cost reduce latency preserve safety respect constraints ``` There may be no single perfect answer. A system may need to reason about trade-offs. --- ## Planning Objectives become useful when they guide action. ```text GOAL ↓ SUBGOALS ↓ PLAN ↓ EXECUTION ↓ CHECK ``` Possible research areas: - decomposition - sequencing - prioritization - replanning - resource allocation - long-horizon planning --- ## Success Criteria A goal without a measurable outcome is difficult to evaluate. Possible questions: - What counts as success? - What counts as partial success? - When should the system stop? - Which metrics matter? - How should trade-offs be scored? --- ## Objective Conflicts AI systems may receive goals that conflict. Example: ```text Objective A: maximize accuracy Objective B: minimize latency Objective C: minimize cost ``` A useful system should make these conflicts visible rather than hide them. --- ## Objective Updates Goals can change during execution. ```text OLD OBJECTIVE ↓ AUTHORIZED UPDATE ↓ NEW OBJECTIVE ↓ REPLAN ``` This connects Objectives naturally with agents, orchestration, corrigibility, evaluation, and planning. --- # Possible Spaces ### Objective Builder Turn a broad intention into structured goals, constraints, and success criteria. ### Multi-Objective Planner Compare plans across quality, cost, time, and risk. ### Goal Decomposer Break one high-level objective into measurable subgoals. ### Objective Conflict Detector Identify competing or contradictory goals. ### Success Criteria Designer Convert vague objectives into measurable evaluation criteria. ### Goal Update Simulator Test how a plan changes when an objective changes. ### Pareto Explorer Visualize trade-offs between multiple objectives. ### Agent Objective Inspector Inspect goals, priorities, constraints, and stop conditions of an agent workflow. --- # Possible Datasets Potential datasets may include: ```text goal-decomposition-tasks multi-objective-scenarios objective-conflicts success-criteria-examples agent-goal-traces planning-objectives goal-update-cases ``` Useful fields may include: - objective - priority - constraint - metric - target - subgoal - tradeoff - outcome - success --- # Possible Models Models may support: - goal extraction - objective classification - subgoal generation - priority ranking - conflict detection - success-criteria generation - plan scoring - multi-objective selection --- # A Simple Objective Record ```json { "objective": "Reduce inference cost", "constraints": [ "quality must remain above threshold", "latency must stay below 2 seconds" ], "metrics": [ "cost_per_request", "quality_score", "latency_ms" ], "success": "20% lower cost without violating constraints" } ``` Clear objectives make evaluation easier. --- # Objectives + Agents Agents need goals. A robust agent may need more than a sentence describing a task. It may need: ```text goal + priority + constraints + success criteria + stop conditions ``` That structure can make behavior easier to inspect and evaluate. --- # Objectives + World Models World models may simulate possible futures. Objectives determine which futures are desirable. ```text WORLD MODEL ↓ POSSIBLE FUTURES ↓ OBJECTIVE FUNCTION ↓ SELECTED PLAN ``` Prediction tells us what might happen. Objectives help decide what should happen. --- # Objectives + Corrigibility Objectives should not become permanently fixed. Authorized users may need to change, narrow, replace, cancel, or constrain them. A well-designed AI system should remain responsive to legitimate objective updates. --- # Objectives + Evaluation Evaluation asks whether a system performed well. Objectives define what **well** means. Without a clear objective, a score can be meaningless. --- # Design Principles ### Make goals explicit Hidden objectives are difficult to inspect. ### Separate goals from constraints What we want and what we must not violate are different. ### Define success Every important objective should have measurable criteria where possible. ### Expose trade-offs Competing goals should be visible. ### Allow updates Objectives may change. ### Evaluate outcomes Intent matters, but results matter too. --- # Who Is Objectives For? Objectives may be useful for: - agent developers - AI researchers - planning systems - orchestration teams - optimization researchers - evaluation teams - robotics developers - enterprise AI builders - open-source contributors --- # Long-Term View As AI systems become more capable, the difficult question may increasingly shift from: > **What can the system do?** to: > **What should the system optimize for?** More intelligence makes objective design more important, not less. --- # Independent Organization **Objectives is an independent Hugging Face community organization.** It is not an official optimization platform, standards body, model provider, research institute, or Hugging Face organization. The name **Objectives** reflects the central idea: > **define what matters, make trade-offs explicit, and connect goals to measurable outcomes.** ---

# OBJECTIVES ### **Align. Plan. Measure. Improve.**